An Unsupervised Three-way Decisions Framework of Overload Preference Based on Adjusted Weight Multi-attribute Decision-making Model

In the process of traffic control, law-enforcement officials are required to accurately evaluate the potential probability of freight-driver's overloading behavior. This study establishes a model of overloading preference assessment on the basis of freight-driver's individual variation. With indexes selecting, the equal-weight and AHP-based adjusted weight decision-making model are used respectively to evaluate freight-driver's overload preference. Synthesizing the results from two models, we present a three-way decisions model to make judgment.